import os
import gym
import torch
import pprint
import argparse
import numpy as np
from torch.utils.tensorboard import SummaryWriter

from tianshou.policy import DQNPolicy
from tianshou.utils.net.common import Net
from tianshou.trainer import offpolicy_trainer
from tianshou.data import Collector, ReplayBuffer
from tianshou.env import DummyVectorEnv, SubprocVectorEnv


def get_args():
    parser = argparse.ArgumentParser()
    # the parameters are found by Optuna
    parser.add_argument('--task', type=str, default='LunarLander-v2')
    parser.add_argument('--seed', type=int, default=0)
    parser.add_argument('--eps-test', type=float, default=0.01)
    parser.add_argument('--eps-train', type=float, default=0.73)
    parser.add_argument('--buffer-size', type=int, default=100000)
    parser.add_argument('--lr', type=float, default=0.013)
    parser.add_argument('--gamma', type=float, default=0.99)
    parser.add_argument('--n-step', type=int, default=4)
    parser.add_argument('--target-update-freq', type=int, default=500)
    parser.add_argument('--epoch', type=int, default=10)
    parser.add_argument('--step-per-epoch', type=int, default=5000)
    parser.add_argument('--collect-per-step', type=int, default=16)
    parser.add_argument('--batch-size', type=int, default=128)
    parser.add_argument('--hidden-sizes', type=int,
                        nargs='*', default=[128, 128])
    parser.add_argument('--dueling-q-hidden-sizes', type=int,
                        nargs='*', default=[128, 128])
    parser.add_argument('--dueling-v-hidden-sizes', type=int,
                        nargs='*', default=[128, 128])
    parser.add_argument('--training-num', type=int, default=10)
    parser.add_argument('--test-num', type=int, default=100)
    parser.add_argument('--logdir', type=str, default='log')
    parser.add_argument('--render', type=float, default=0.)
    parser.add_argument(
        '--device', type=str,
        default='cuda' if torch.cuda.is_available() else 'cpu')
    return parser.parse_args()


def test_dqn(args=get_args()):
    env = gym.make(args.task)
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    # train_envs = gym.make(args.task)
    # you can also use tianshou.env.SubprocVectorEnv
    train_envs = DummyVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.training_num)])
    # test_envs = gym.make(args.task)
    test_envs = SubprocVectorEnv(
        [lambda: gym.make(args.task) for _ in range(args.test_num)])
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    train_envs.seed(args.seed)
    test_envs.seed(args.seed)
    # model
    Q_param = {"hidden_sizes": args.dueling_q_hidden_sizes}
    V_param = {"hidden_sizes": args.dueling_v_hidden_sizes}
    net = Net(args.state_shape, args.action_shape,
              hidden_sizes=args.hidden_sizes, device=args.device,
              dueling_param=(Q_param, V_param)).to(args.device)
    optim = torch.optim.Adam(net.parameters(), lr=args.lr)
    policy = DQNPolicy(
        net, optim, args.gamma, args.n_step,
        target_update_freq=args.target_update_freq)
    # collector
    train_collector = Collector(
        policy, train_envs, ReplayBuffer(args.buffer_size))
    test_collector = Collector(policy, test_envs)
    # policy.set_eps(1)
    train_collector.collect(n_step=args.batch_size)
    # log
    log_path = os.path.join(args.logdir, args.task, 'dqn')
    writer = SummaryWriter(log_path)

    def save_fn(policy):
        torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))

    def stop_fn(mean_rewards):
        return mean_rewards >= env.spec.reward_threshold

    def train_fn(epoch, env_step):  # exp decay
        eps = max(args.eps_train * (1 - 5e-6) ** env_step, args.eps_test)
        policy.set_eps(eps)

    def test_fn(epoch, env_step):
        policy.set_eps(args.eps_test)

    # trainer
    result = offpolicy_trainer(
        policy, train_collector, test_collector, args.epoch,
        args.step_per_epoch, args.collect_per_step, args.test_num,
        args.batch_size, train_fn=train_fn, test_fn=test_fn,
        stop_fn=stop_fn, save_fn=save_fn, writer=writer,
        test_in_train=False)

    assert stop_fn(result['best_reward'])
    if __name__ == '__main__':
        pprint.pprint(result)
        # Let's watch its performance!
        policy.eval()
        policy.set_eps(args.eps_test)
        test_envs.seed(args.seed)
        test_collector.reset()
        result = test_collector.collect(n_episode=[1] * args.test_num,
                                        render=args.render)
        print(f'Final reward: {result["rew"]}, length: {result["len"]}')


if __name__ == '__main__':
    test_dqn(get_args())
